code: def predict_step_output_dir(
      tmp_recipe_root_path: Path, tmp_recipe_exec_path: Path
  ):
      ingest_scoring_step_output_dir = tmp_recipe_exec_path.joinpath(
          "steps", "ingest_scoring", "outputs"
      )
      ingest_scoring_step_output_dir.mkdir(parents=True)
      X, _ = load_diabetes(as_frame=True, return_X_y=True)
      X.to_parquet(
          ingest_scoring_step_output_dir.joinpath(_INPUT_FILE_NAME)
      )
      predict_step_output_dir = tmp_recipe_exec_path.joinpath(
          "steps", "predict", "outputs"
      )
      predict_step_output_dir.mkdir(parents=True)
      recipe_yaml = tmp_recipe_root_path.joinpath(
          _RECIPE_CONFIG_FILE_NAME
      )
      recipe_yaml.write_text(
          f"""
  recipe: "regression/v1"
  experiment:
    name: "test"
    tracking_uri: {mlflow.get_tracking_uri()}
  """
      )
      return predict_step_output_dir
  
imports: import os
  import tempfile
  from pathlib import Path
  from unittest import mock
  import pandas
  import pytest
  from pyspark.sql import SparkSession
  from sklearn.datasets import load_diabetes
  import mlflow
  from mlflow.exceptions import MlflowException
  from mlflow.recipes.artifacts import RegisteredModelVersionInfo
  from mlflow.recipes.steps.predict import _INPUT_FILE_NAME, _SCORED_OUTPUT_FILE_NAME, PredictStep
  from mlflow.recipes.steps.register import _REGISTERED_MV_INFO_FILE
  from mlflow.recipes.utils import _RECIPE_CONFIG_FILE_NAME
  from mlflow.utils.file_utils import read_yaml
  from tests.recipes.helper_functions import get_random_id, train_and_log_model, train_log_and_register_model
label_desc: Use image node_id 4 for calling a global function with example usage: predict_step_output_dir(tmp_recipe_root_path, tmp_recipe_exec_path) and returns: predict_step_output_dir